1 Regional Meteorological Centre, Civil Aerodrome, Guwahati, India 2 India Meteorological Department, Pune-5, India Long Range Forecasting Indian Summer Monsoon Rainfall by a Hybrid Principal Component Neural Network Model P. Guhathakurta 1 , M. Rajeevan 2 , and V. Thapliyal 2 With 8 Figures Received August 20, 1998 Revised April 20, 1999 Summary The existing methods based on statistical techniques for long range forecasts of Indian monsoon rainfall have shown reasonably accurate performance, for last 11 years. Because of the limitation of such statistical techniques, new techniques may have to be tried to obtain better results. In this paper, we discuss the results of an arti®cial neural network model by combining two different neural networks, one explaining assumed deterministic dynamics within the time series of Indian monsoon rainfall (Model I) and other using eight regional and global predictors (Model II). The model I has been developed by using the data of past 50 years (1901±50) and the data for recent period (1951±97) has been used for veri®cation. The model II has been developed by using the 30 year (1958±87) data and the veri®cation of this model has been carried out using the independent data of 10 year period (1988±97). In model II, instead of using eight parameters directly as inputs, we have carried out Principal Component Analysis (PCA) of the eight parameters with 30 years of data, 1958±87, and the ®rst ®ve principal components are included as input parameters. By combining model I and model II, a hybrid principal component neural network model (Model III) has been developed by using 30 year (1958±87) data as training period and recent 10 year period (1988±97) as veri®cation period. Performance of the hybrid model (Model III) has been found the best among all three models developed. Root mean square error (RMSE) of this hybrid model during the independent period (1988±97) is 4.93% as against 6.83% of the operational forecasts of the India Meteor- ological Department (IMD) using the 16 parameter Power Regression model. As this hybrid model is showing good results, it is now used by the IMD for experimental long- range forecasts of summer monsoon rainfall over India as a whole. 1. Introduction Long range forecasts of Indian summer monsoon rainfall (June±September) have been very crucial for proper agricultural planning in India. Besides, the summer monsoon rainfall activity over India also has considerable impact on other national activities such as power generation, water resources etc. After the initial work of Walker (1910), several attempts (Thapliyal, 1982; Gowariker et al., 1989, 1991) have been made for developing better models of long range forecasts of summer mon- soon rainfall of India. Performance of the Param- etric and Power Regression models (Gowariker et al., 1991) have been satisfactory and reason- ably accurate during last 11 years. These models are used by the IMD for long range forecasts of summer monsoon (June±September) rainfall over India as a whole. Since these statistical models have some inherent limitations, emphasize is given to develop better and alternate techniques for long range forecasts of summer monsoon rainfall of India. In this paper, an attempt is made to explore the potential of neural network technique for long range forecasts of Indian summer monsoon Meteorol. Atmos. Phys. 71, 255±266 (1999)